Sweden’s property sector faces many challenges, including rising energy prices, high greenhouse gas emissions, and a slow renovation rate. Building owners usually lack relevant data on existing buildings and struggle to develop renovation strategies under rapidly changing boundary conditions. This project addresses these problems by leveraging machine learning (ML) and digitalisation.
A previously developed workflow to generate 3D thermal simulation models is extended by a novel workflow to replace time-consuming physics-based energy simulations with ML. The novel workflow will also include embodied carbon to ensure the renovation reduces greenhouse gas emissions from a life-cycle perspective. This will let building owners and facility managers quickly assess many potential renovation strategies across scenarios in seconds. The workflow will be developed in close collaboration with stakeholders and operationalised in a web tool to be used by building owners.
Project partners
